A Distribution Network Monitoring and Processing Method and System Based on a Single Grid Map

Abstract

The present invention provides a distribution network monitoring and processing method and system based on a single map of the power grid, belonging to the technical field of power grid monitoring. Specifically, it includes: determining historical similar reference dates for different dates based on weather data, and when determining that the date when the non-fault probability of the area does not meet the requirements according to the historical fault data under different historical similar reference dates, based on the number of historical similar reference dates of different dates and the deviation of the weather data from different historical reference dates, determining the reference value types of different dates, obtaining the fault probabilities of the dates under different reference value types, and combining the power supply data in the area to determine the update processing target of the power grid map, and performing monitoring and processing of the distribution network after the update processing target is updated, thereby improving the reliability of the monitoring and processing.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid monitoring, and particularly relates to a distribution network monitoring and processing method and system based on a single power grid map. Background Art

[0002] Since the power grid will inevitably change during operation, this may cause the power grid map to not accurately reflect the real power grid operation status, and it is impossible to accurately monitor and process the distribution network. Therefore, in order to monitor and process the distribution network, in the invention patent application CN202411634053.9 "Intelligent Monitoring Method for Distribution Network Based on Data Middle Platform and Edge Computing", a causal relationship map of the distribution network is constructed, and based on the final detection result, abnormal analysis is carried out. According to the final detection result and the abnormal analysis result, a maintenance report is generated. However, the above technical solutions all have the following technical problems:

[0003] Since the amount of data for updating the power grid map is large, and due to the differences in power supply topologies in different regions of the power grid map, even under the same weather conditions, there are deviations in the probability of power supply failures in different regions. Therefore, how to update the power grid map by combining the power supply failure probability under future weather data to ensure the reliability of the monitoring and processing of the distribution network has become a technical problem to be solved urgently.

[0004] In view of the above technical problems, specifically, the present application provides a distribution network monitoring and processing method and system based on a single power grid map. Summary of the Invention

[0005] To achieve the object of the present invention, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present application provides a distribution network monitoring and processing method based on a single power grid map, specifically including:

[0007] S1 Divide the power grid map into multiple regions. When it is determined that the region does not belong to the priority update region based on the historical operation data and fault influence range of power equipment in different regions, obtain the weather data of different dates within a preset future time period.

[0008] S2 Based on the weather data, determine the historical similarity reference dates of different dates. When it is determined that there is no date with a fault probability not meeting the requirements based on the historical fault data under different historical similarity reference dates, proceed to the next step.

[0009] S3 Based on the number of historical similarity reference dates of different dates and the deviation of the weather data from different historical reference dates, determine the reference value types of different dates.

[0010] S4 obtains the failure probabilities of dates under different reference value types, and combines the power supply data in the region to determine the update processing target of the power grid map. After the update processing target is updated, the monitoring process of the distribution network is carried out, and after the update processing target is updated, the monitoring process of the distribution network is carried out.

[0011] The beneficial effects of the present invention are as follows:

[0012] According to the historical failure data under different historical similar reference dates, it is determined whether there are dates in the region where the failure probability does not meet the requirements, thus realizing the determination of the failure probabilities of the region on different dates from the perspective of weather data, fully considering the differences in the power supply reliability of different regions due to the differences in power supply equipment and power supply topologies, and also laying a foundation for the differential update processing of the power grid maps of different regions.

[0013] Based on the failure probabilities of dates under different reference value types and the power supply data, the update processing target of the power grid map is determined. Not only the differences in the failure probabilities of the region on different dates are considered singly, realizing the comprehensive consideration of the probability of failures occurring in the region in the future period, but also the power supply data is further combined, realizing the comprehensive consideration of the power supply amounts of different regions, realizing the differential update processing of the power grid maps of different regions, and at the same time, through the update processing of the power grid map, the reliability of the monitoring process of the distribution network is ensured.

[0014] A further technical solution lies in dividing the power grid map into multiple regions, specifically including:

[0015] The power grid map is divided into multiple regions according to a preset area threshold.

[0016] A further technical solution lies in that the power equipment includes transformers, circuit breakers, disconnectors, load switches, and reactors.

[0017] A further technical solution lies in that the historical operation data includes the number of failures of different failure types.

[0018] A further technical solution lies in that the failure influence range is determined according to the node positions of the power topologies where the power equipment is located, specifically including the number of power users affected by the failure and the historical power consumption loads of different power users.

[0019] A further technical solution lies in determining that the region does not belong to the priority update region, specifically including:

[0020] Based on the historical operation data of the power equipment in the region, the historical number of failures of the power equipment in the region is determined;

[0021] Determine the abnormal power equipment in the power equipment according to the historical failure times of the power equipment;

[0022] Based on the fault influence ranges of different power equipment, determine the total number of power users affected by the faults of different abnormal power equipment, and use the total number of power users to determine whether the area belongs to a priority update area.

[0023] A further technical solution is that when the total number of power users is greater than a preset number of users, it is determined that the area belongs to a priority update area.

[0024] A further technical solution is that when the area belongs to a priority update area, the area is used as the target for data update processing.

[0025] A further technical solution is that the method for determining the update processing target of the power grid map is as follows:

[0026] Based on the reference value types of different dates, determine the preset coefficients of different dates, and determine the corrected fault probabilities of different dates based on the product of the preset coefficients and the fault probabilities;

[0027] Take the dates with the corrected fault probabilities greater than the preset probability threshold as high-probability dates, and determine the area fault anomaly coefficient of the area according to the proportion of the number of high-probability dates;

[0028] Based on the power supply data in the area, determine the daily average power supply in the area, and use the preset weight coefficient corresponding to the daily average power supply to determine the power consumption weight coefficient of the area. Based on the product of the power consumption weight coefficient and the area fault anomaly coefficient, determine the corrected anomaly coefficient of the area, and use the corrected anomaly coefficient to determine the update processing target of the power grid map.

[0029] A further technical solution is that using the corrected anomaly coefficient to determine the update processing target of the power grid map specifically includes:

[0030] Take the areas with the corrected anomaly coefficients greater than the preset corrected coefficient threshold as the targets for data update processing.

[0031] In a second aspect, the present application provides a computer system, including: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor, wherein when the processor runs the computer program, it executes the above-mentioned power distribution network monitoring and processing method based on a single power grid map.

[0032] Other features and advantages will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention are realized and attained by the structure particularly pointed out in the specification and the drawings.

[0033] To make the above objects, features and advantages of the present invention more comprehensible, the following specific preferred embodiments are given in conjunction with the accompanying drawings and described in detail as follows. Description of the Drawings

[0034] The above and other features and advantages of the present invention will become more apparent by referring to the following detailed description of its exemplary embodiments with reference to the accompanying drawings.

[0035] Figure 1 is a flowchart of a distribution network monitoring and processing method based on a single power grid map;

[0036] Figure 2 is a flowchart for determining that a region does not belong to a priority update region;

[0037] Figure 3 is a flowchart of a method for determining the failure probability of a date;

[0038] Figure 4 is a flowchart of a method for determining the update processing target of a power grid map. Detailed Embodiments

[0039] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. Like reference numerals in the figures denote like or similar structures and thus their detailed description will be omitted.

[0040] The terms "a", "an", "the", and "said" are used to denote the presence of one or more elements / components / etc.; the terms "comprising" and "having" are used to mean an open inclusion and mean that in addition to the listed elements / components / etc., there may be additional elements / components / etc.

[0041] Embodiment 1

[0042] To solve the above problems, according to one aspect of the present invention, as Figure 1 shown, according to one aspect of the present invention, there is provided a distribution network monitoring and processing method based on a single power grid map, specifically including:

[0043] S1 Divide the power grid map into multiple regions. When it is determined that the region does not belong to the priority update region based on the historical operation data and fault impact scope of the power equipment in different regions, obtain the weather data for different dates within a preset future time period.

[0044] Further, dividing the power grid map into multiple regions specifically includes:

[0045] Divide the power grid map into multiple regions according to a preset area threshold.

[0046] Specifically, the power equipment includes transformers, circuit breakers, disconnectors, load switches, and reactors.

[0047] It should be noted that the historical operation data includes the number of faults of different fault types.

[0048] It can be understood that the fault impact scope is determined according to the node position of the power topology where the power equipment is located, specifically including the number of power users affected by the fault and the historical power consumption loads of different power users.

[0049] Specifically, as Figure 2 shown, determining that the region does not belong to the priority update region specifically includes:

[0050] Based on the historical operation data of the power equipment in the region, determine the historical number of faults of the power equipment in the region;

[0051] According to the historical number of faults of the power equipment, determine the abnormal power equipment among the power equipment;

[0052] Based on the fault impact scopes of different power equipment, determine the total number of power users affected by the faults of different abnormal power equipment, and use the total number of power users to determine whether the region belongs to the priority update region.

[0053] Further, when the total number of power users is greater than the preset number of users, it is determined that the region belongs to the priority update region.

[0054] It should be noted that when the region belongs to the priority update region, the region is used as the target for data update processing.

[0055] Optionally, determining that the region does not belong to the priority update region specifically includes:

[0056] S11 Based on the historical operation data of the power equipment in the region, determine the historical number of faults of the power equipment in the region, and use the historical number of faults of the power equipment to determine the fault coefficient of the power equipment;

[0057] S12 Determine the number of power users affected by the faults of different power equipment based on the fault impact scope of different power equipment, and determine the impact weight coefficients of different power equipment by using the number of power users affected by the faults;

[0058] S13 Determine the fault impact coefficient according to the impact weight coefficients and fault coefficients of different power equipment, and determine whether the area belongs to the priority update area by using the fault impact coefficient.

[0059] Furthermore, the fault impact coefficient is the sum of the products of the impact weight coefficients and fault coefficients of different power equipment.

[0060] It should be noted that when the fault impact coefficient is greater than the preset impact coefficient threshold, it is determined that the area belongs to the priority update area.

[0061] Optionally, the above step S11 includes the following content:

[0062] S111 Based on the historical operation data of the power equipment in the area, determine the power equipment with fault data in the area. When the number of power equipment with fault data in the area is less than the preset equipment number, it is determined that the area does not belong to the priority update area. When the number of power equipment with fault data in the area is not less than the preset equipment number, go to step S112;

[0063] S112 When the number of power equipment with fault data in the area is greater than the preset number threshold, it is determined that the area belongs to the priority update area. When the number of power equipment with fault data in the area is not greater than the preset number threshold, go to step S113;

[0064] S113 Determine the historical fault times of the power equipment in the area, and determine the fault coefficient of the power equipment by using the historical fault times of the power equipment. When there is no power equipment with a fault coefficient greater than the preset fault coefficient, go to step S12. When there is a power equipment with a fault coefficient greater than the preset fault coefficient, go to step S114;

[0065] S114 When the number of power equipment with a fault coefficient greater than the preset fault coefficient does not meet the requirements, it is determined that the area belongs to the priority update area. When the number of power equipment with a fault coefficient greater than the preset fault coefficient meets the requirements, go to step S12.

[0066] Optionally, the above step S12 includes the following content:

[0067] S121 regards the power equipment with faulty data as historical faulty equipment, determines the number of power users affected by the faults of different historical faulty equipment based on the fault influence scopes of different historical faulty equipment. When the total number of power users affected by the faults of different historical faulty equipment is greater than the preset user number threshold, it is determined that the area belongs to the priority update area. When the total number of power users affected by the faults of different historical faulty equipment is not greater than the preset user number threshold, it proceeds to step S122;

[0068] S122 determines the influence weight coefficients of different power equipment by using the number of power users affected by the faults. When the sum of the influence weight coefficients of different historical faulty equipment is greater than the preset weight coefficient threshold, it is determined that the area belongs to the priority update area. When the sum of the influence weight coefficients of different historical faulty equipment is not greater than the preset weight coefficient threshold, it proceeds to step S123;

[0069] S123 determines the corrected fault coefficients of different historical faulty equipment based on the product of the influence weight coefficients and the fault coefficients of different historical faulty equipment. When the number of historical faulty equipment with corrected fault coefficients not meeting the requirements is greater than the preset faulty equipment number, it is determined that the area belongs to the priority update area. When the number of historical faulty equipment with corrected fault coefficients not meeting the requirements is not greater than the preset faulty equipment number, it proceeds to step S13.

[0070] S2 determines the historical similar reference dates of different dates based on the weather data. When it is determined according to the historical fault data under different historical similar reference dates that there are no dates with fault probabilities not meeting the requirements in the area, it proceeds to the next step;

[0071] Further, the preset time period includes one month, two months, and three months.

[0072] Specifically, the weather data includes temperature, humidity, wind speed, rainfall, and snowfall.

[0073] Optionally, the historical similar reference date is a historical date whose deviation amounts of weather data in different dimensions from the date are all within the preset deviation amount range.

[0074] Further, the historical fault data includes the number of faults under different fault types on different historical similar reference dates.

[0075] Specifically, as Figure 3 shown, the method for determining the fault probability of the date is as follows:

[0076] Based on the historical fault data under different historical similar reference dates, determine the historical similar reference dates with faulty data and use them as the fault reference dates;

[0077] Based on the historical fault data corresponding to different fault reference dates, determine the number of faults for different fault reference dates within different time periods, and determine the fault coefficient for different fault reference dates based on the number of faults within different time periods;

[0078] Based on the average value of the fault coefficients for different fault reference dates and the proportion of the number of fault reference dates in the historical similar reference dates, determine the fault probability for the date.

[0079] Furthermore, the fault probability is the product of the average value of the fault coefficients for different fault reference dates and the proportion of the number of fault reference dates in the historical similar reference dates.

[0080] It should be noted that the value range of the fault probability for the date is between 0 and 1. When the fault probability for the date is greater than the preset fault probability threshold, it is determined that the fault probability for the date does not meet the requirements.

[0081] Optionally, when there are dates in the area for which the fault probability does not meet the requirements, the area is taken as the target for data update processing.

[0082] In another embodiment, the method for determining the fault probability for the date is as follows:

[0083] Based on the historical fault data under different historical similar reference dates, determine the number of faults under different historical similar reference dates. When the total number of faults under different historical similar reference dates does not meet the requirements, it is determined that the fault probability for the date does not meet the requirements;

[0084] When the total number of faults under different historical similar reference dates meets the requirements:

[0085] Based on the number of faults under different historical reference dates, when there are historical reference dates for which the number of faults does not meet the requirements, it is determined that the fault probability for the date does not meet the requirements;

[0086] When there are no historical reference dates for which the number of faults does not meet the requirements:

[0087] Determine the historical similar reference dates with fault data and use them as fault reference dates. When the average value of the number of faults for different fault reference dates is within the preset number range:

[0088] When the proportion of the number of the fault reference dates in the historical similar reference dates is greater than the preset date proportion: it is determined that the fault probability for the date does not meet the requirements;

[0089] When the average number of failures for different failure reference dates is not within the preset number range or the proportion of the number of failure reference dates among the historical similar reference dates is not greater than the preset date proportion:

[0090] Based on the historical failure data of different failure reference dates, determine the number of failures of different failure reference dates in different time periods, and determine the failure coefficient of different failure reference dates based on the number of failures in different time periods. When there is a failure reference date whose failure coefficient does not meet the requirements, it is determined that the failure probability of the date does not meet the requirements;

[0091] When there is no failure reference date whose failure coefficient does not meet the requirements:

[0092] Take the failure reference dates with failure coefficients within the preset failure coefficient range as screening reference dates. When the proportion of the number of the screening reference dates among the historical similar reference dates does not meet the requirements, it is determined that the failure probability of the date does not meet the requirements;

[0093] When the proportion of the number of the screening reference dates among the historical similar reference dates meets the requirements:

[0094] Determine the failure probability of the date according to the average value of the failure coefficients of different failure reference dates and the proportion of the number of failure reference dates among the historical similar reference dates.

[0095] S3 Determine the reference value type of different dates based on the number of historical similar reference dates of different dates and the deviation of weather data from different historical reference dates;

[0096] Furthermore, the method for determining the reference value type of the date is as follows:

[0097] Based on the deviation of the weather data of the historical similar reference dates of the date, determine the deviation coefficients of the weather data of different historical similar reference dates in different dimensions;

[0098] Based on the average value of the deviation coefficients of the weather data in different dimensions, determine the reference value coefficients of different historical similar reference dates;

[0099] Based on the sum of the reference value coefficients of different historical similar reference dates, determine the value coefficient sum of different dates, and use the value coefficient sum to determine the reference value type of the date.

[0100] Specifically, the deviation coefficient is determined according to the ratio of the deviation amount of the weather data in different dimensions to the monitoring data of the weather data of the date.

[0101] It should be noted that the reference value coefficient is the difference between the preset value and the average of the deviation coefficients of weather data in different dimensions.

[0102] Optionally, using the value coefficient and determining the reference value type of the date specifically includes:

[0103] Taking the value coefficient and the corresponding preset reference value type as the reference value type of the date.

[0104] Specifically, the reference value type includes a first-class value type, a second-class value type, and a third-class value type, where the reference value of the first-class value type is greater than that of the second-class value type, and the second-class value type is greater than the third-class value type.

[0105] S4 Obtain the failure probability of the date under different reference value types, and in combination with the power supply data in the region, determine the update processing target of the power grid diagram, and perform monitoring processing on the distribution network after the update processing target is updated.

[0106] Specifically, as Figure 4 shown, the method for determining the update processing target of the power grid diagram is:

[0107] Based on the reference value types of different dates, determine the preset coefficients of different dates, and determine the corrected failure probabilities of different dates based on the product of the preset coefficients and the failure probabilities;

[0108] Taking the dates with the corrected failure probabilities greater than the preset probability threshold as high-probability dates, and determining the regional failure anomaly coefficient of the region according to the proportion of the number of high-probability dates;

[0109] Based on the power supply data in the region, determine the average daily power supply in the region, and use the preset weight coefficient corresponding to the average daily power supply to determine the power consumption weight coefficient of the region. Based on the product of the power consumption weight coefficient and the regional failure anomaly coefficient, determine the corrected anomaly coefficient of the region, and use the corrected anomaly coefficient to determine whether the region is the update processing target of the power grid diagram.

[0110] Furthermore, using the corrected anomaly coefficient to determine the update processing target of the power grid diagram specifically includes:

[0111] Taking the regions with the corrected anomaly coefficients greater than the preset correction coefficient threshold as the update processing targets of the data.

[0112] In another possible embodiment, the method for determining the update processing target of the power grid diagram is:

[0113] Based on the reference value types of different dates, determine the date under a certain value type. When the number of dates with a failure probability greater than the preset probability limit value under a certain value type does not meet the requirements, then use the said area as the update processing target of the power grid map;

[0114] When the number of dates with a failure probability greater than the preset probability limit value under a certain value type meets the requirements:

[0115] Based on the reference value types of different dates, determine the preset coefficients of different dates. Based on the product of the preset coefficient and the failure probability, determine the corrected failure probabilities of different dates. When the average value of the corrected failure probabilities of different dates is greater than the failure probability limit value, then use the said area as the update processing target of the power grid map;

[0116] When the average value of the corrected failure probabilities of different dates is not greater than the failure probability limit value:

[0117] When the number of dates with corrected failure probabilities within the preset failure probability interval is within the preset date number interval:

[0118] Based on the power supply data in the said area, determine the average daily power supply in the said area. When the average daily power supply is greater than the preset power supply threshold value, then use the said area as the update processing target of the power grid map;

[0119] When the number of dates with corrected failure probabilities within the preset failure probability interval is not within the preset date number interval or when the average daily power supply is not greater than the preset power supply threshold value:

[0120] Use the dates with corrected failure probabilities greater than the preset probability threshold value as high-probability dates, and determine the area failure anomaly coefficient of the said area according to the proportion of the number of high-probability dates;

[0121] Use the preset weight coefficient corresponding to the average daily power supply to determine the power consumption weight coefficient of the said area. Based on the product of the power consumption weight coefficient and the area failure anomaly coefficient, determine the corrected anomaly coefficient of the said area, and use the corrected anomaly coefficient to determine whether the said area is the update processing target of the power grid map.

[0122] Embodiment 2

[0123] In a second aspect, the present application provides a computer system, including: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the above-mentioned power distribution network monitoring and processing method based on a single power grid map when running the computer program.

[0124] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the device, equipment, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the partial description of the method embodiments.

[0125] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0126] The above is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A distribution network monitoring and processing method based on a power grid map, characterized in that: Specifically include: Divide the power grid map into multiple areas, and based on the historical operation data of the power equipment in different areas and the scope of fault impact, when it is determined that the area does not belong to the priority update area, obtain weather data for different dates in a future preset time period; Determine historical similar reference dates of different dates based on the weather data, and when it is determined that there is no date in the region where the probability of failure does not meet the requirements according to the historical failure data under different historical similar reference dates, proceed to the next step; Determine the reference value type for different dates based on the number of historically similar reference dates for different dates and the deviations from the weather data for different historical reference dates; Obtaining the failure probability of a date under different reference value types, and combining the power supply data in the area, determining the update processing target of the power grid diagram, and performing distribution network monitoring after the update processing target is updated; Determining that the area does not belong to the priority update area includes: Determining the number of historical failures of the power equipment in the area based on historical operation data of the power equipment in the area; Determining abnormal power equipment among the power equipment according to the historical failure times of the power equipment; Based on the fault impact range of different power equipment, determine the total number of power users affected by the faults of different abnormal power equipment, and use the total number of power users to determine whether the area belongs to the priority update area; The method for determining the update processing target of the power grid diagram is: Determining preset coefficients for different dates based on the reference value types for different dates, and determining corrected failure probabilities for different dates based on the product of the preset coefficients and the failure probability; The date when the corrected fault probability is greater than a preset probability threshold is taken as a high-probability date, and the regional fault anomaly coefficient of the region is determined according to the proportion of the number of high-probability dates; Based on the power supply data in the area, the average daily power supply in the area is determined, and the power consumption weight coefficient of the area is determined using the preset weight coefficient corresponding to the average daily power supply. The corrected abnormality coefficient of the area is determined based on the product of the power consumption weight coefficient and the regional fault abnormality coefficient, and the updated processing target of the power grid diagram is determined using the corrected abnormality coefficient.

2. The distribution network monitoring and processing method based on a power grid one map according to claim 1, characterized in that: The power grid diagram is divided into multiple areas, including: The power grid diagram is divided into a plurality of areas according to a preset area threshold.

3. The distribution network monitoring and processing method based on a power grid one map according to claim 1, characterized in that: The electric power equipment includes a transformer, a circuit breaker, an isolating switch, a load switch and a reactor.

4. The distribution network monitoring and processing method based on a power grid one map according to claim 1, characterized in that: The historical operation data includes the number of failures of different failure types.

5. The distribution network monitoring and processing method based on a power grid one map according to claim 1, characterized in that: When the area belongs to a priority update area, the area is used as a data update processing target.

6. The distribution network monitoring and processing method based on a power grid one map according to claim 1, characterized in that: The preset time period includes one month, two months and three months.

7. The distribution network monitoring and processing method based on a power grid one map according to claim 6, characterized in that: Determining the update processing target of the power grid diagram by using the corrected abnormality coefficient specifically includes: The area where the correction abnormality coefficient is greater than the preset correction coefficient threshold is used as the data update processing target.

8. A computer system comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, a distribution network monitoring and processing method based on a single map of the power grid as described in any one of claims 1-7 is executed.

Images